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BC super-transcribe

Unified speech-to-text skill. Use when the user asks to transcribe audio or video, generate subtitles, identify speakers, translate speech, search transcripts, diarize meetings, or perform any speech-to-text task. Also use when a voice message or audio file appears in chat and the user's intent to transcribe it is very clear.

modbender/skill-library-mcp Agent Skills author: modbender MIT 18 files · 3 scripts body ≈ 17 428 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Unified speech-to-text skill.

As a process C 62/100 · Has gaps — weak spots: inputs and preconditions, execution cost, running it twice

GeneratorYouTubeSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Execution cost w 6
10
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-pipe-to-shell-known-host setup.sh:173
    Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)
    info "Install uv for faster setup: curl -LsSf https://astral.sh/uv/install.sh | sh"
    code literal

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 17428 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 62/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 10Execution cost. Instruction body is 17428 tokens: crowds the task out of the window
  • 30Running it twice. 9 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 60Steps. 149 steps, 7 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 26 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • -263 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 327: enough signal without eating the budget
  • +4Structure: 64 headings
  • +3Step-by-step instructions: 149 items
  • +3Output format is stated explicitly
  • +4Has examples (23 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.